
We present a single GAN model capable of implementing image-to-image synthesis. We combine the benefits of established FR-IQA metrics [1]}, [2]}, [3]} and the low-level salient features of a deep convolutional neural network (DCNN) to aid adversarial image synthesis aimed at producing perceptually appealing images. In our model, we introduced an attention schema that exploits the salient perceptual features in a channel-wise fashion and the spatial map representation embeddings of standard FR-IQA metrics (SSIM, MDSI and FSIM). Our framework consists of five main components; a quality-aware generator network \({G:(g_1, g_2)}\) , where \(g_1\)  and \(g_2\)  represent the encoder and decoder section, respectively.
\(g_1\)  and \(g_2\)  are coupled with a perceptual attentive mechanism (PAM) for quality encoding and a perceptual quality map fusion network \({M_F}\)  at the latent space as shown in Figure 1. The discriminative networks \({D:(d_1, d_2)}\)  critics images generated by \({G:(g_1, g_2)}\)  in an adversarial manner without compromising image quality and the perceptual consistency with the reference image. The perceptual quality map generator combines the core quality metric functions that capture the sensitive perceptual features of a given image, while the score regression network pools the images synthesized by \({G:(g_1, g_2)}\)  to estimate reference quality score \(s\) . Our overall objective consists of a Wasserstein Gradient Penalty, Structural Similarity Index Gradient Penalty (SSIM-GP) and a Natural Image Quality Estimator (NIQE) as defined in section 4.
